Overview

Dataset statistics

Number of variables21
Number of observations87
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory176.0 B

Variable types

Categorical3
Text1
Unsupported4
Numeric13

Alerts

class has constant value "Healthy"Constant
flag has constant value "real"Constant
file_path has unique valuesUnique
rms_acc_x has unique valuesUnique
rms_acc_y has unique valuesUnique
path has unique valuesUnique
samp_en_x has unique valuesUnique
samp_en_y has unique valuesUnique
f80_x has unique valuesUnique
f80_y has unique valuesUnique
mf_lf_x has unique valuesUnique
mf_lf_y has unique valuesUnique
mf_mf_x has unique valuesUnique
mf_mf_y has unique valuesUnique
mf_hf_x has unique valuesUnique
mf_hf_y has unique valuesUnique
cop_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
cop_y is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_y is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-22 08:30:15.726649
Analysis finished2024-05-22 08:30:30.659745
Duration14.93 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

class
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Healthy
87 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters609
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealthy
2nd rowHealthy
3rd rowHealthy
4th rowHealthy
5th rowHealthy

Common Values

ValueCountFrequency (%)
Healthy 87
100.0%

Length

2024-05-22T04:30:30.731766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:30:30.837785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
healthy 87
100.0%

Most occurring characters

ValueCountFrequency (%)
H 87
14.3%
e 87
14.3%
a 87
14.3%
l 87
14.3%
t 87
14.3%
h 87
14.3%
y 87
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 87
14.3%
e 87
14.3%
a 87
14.3%
l 87
14.3%
t 87
14.3%
h 87
14.3%
y 87
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 87
14.3%
e 87
14.3%
a 87
14.3%
l 87
14.3%
t 87
14.3%
h 87
14.3%
y 87
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 87
14.3%
e 87
14.3%
a 87
14.3%
l 87
14.3%
t 87
14.3%
h 87
14.3%
y 87
14.3%

file_path
Text

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:31.034833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length58
Mean length56.321839
Min length55

Characters and Unicode

Total characters4900
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)100.0%

Sample

1st row../data/raw/COP_Tests/HG\participant_21\OASETD_SWARII.txt
2nd row../data/raw/COP_Tests/HG\participant_21\OASE_SWARII.txt
3rd row../data/raw/COP_Tests/HG\participant_21\OASITD_SWARII.txt
4th row../data/raw/COP_Tests/HG\participant_21\OCSETD_SWARII.txt
5th row../data/raw/COP_Tests/HG\participant_21\OCSE_SWARII.txt
ValueCountFrequency (%)
data/raw/cop_tests/hg\participant_21\oasetd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_23\ocsitd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_21\oasitd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_21\ocsetd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_21\ocse_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_21\ocsitd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_21\ocsi_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_22\eboasitd_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_22\eboasi_swarii.txt 1
 
1.1%
data/raw/cop_tests/hg\participant_22\eboatd_swarii.txt 1
 
1.1%
Other values (77) 77
88.5%
2024-05-22T04:30:31.392919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 522
 
10.7%
a 435
 
8.9%
/ 348
 
7.1%
. 261
 
5.3%
_ 261
 
5.3%
I 217
 
4.4%
p 174
 
3.6%
\ 174
 
3.6%
s 174
 
3.6%
i 174
 
3.6%
Other values (31) 2160
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 522
 
10.7%
a 435
 
8.9%
/ 348
 
7.1%
. 261
 
5.3%
_ 261
 
5.3%
I 217
 
4.4%
p 174
 
3.6%
\ 174
 
3.6%
s 174
 
3.6%
i 174
 
3.6%
Other values (31) 2160
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 522
 
10.7%
a 435
 
8.9%
/ 348
 
7.1%
. 261
 
5.3%
_ 261
 
5.3%
I 217
 
4.4%
p 174
 
3.6%
\ 174
 
3.6%
s 174
 
3.6%
i 174
 
3.6%
Other values (31) 2160
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 522
 
10.7%
a 435
 
8.9%
/ 348
 
7.1%
. 261
 
5.3%
_ 261
 
5.3%
I 217
 
4.4%
p 174
 
3.6%
\ 174
 
3.6%
s 174
 
3.6%
i 174
 
3.6%
Other values (31) 2160
44.1%

exam
Categorical

Distinct16
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
OASETD
OASE
OASITD
OCSETD
OCSE
Other values (11)
47 

Length

Max length8
Median length6
Mean length5.2873563
Min length4

Characters and Unicode

Total characters460
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOASETD
2nd rowOASE
3rd rowOASITD
4th rowOCSETD
5th rowOCSE

Common Values

ValueCountFrequency (%)
OASETD 8
9.2%
OASE 8
9.2%
OASITD 8
9.2%
OCSETD 8
9.2%
OCSE 8
9.2%
OCSITD 8
9.2%
OCSI 8
9.2%
OASI 7
 
8.0%
EBOASITD 3
 
3.4%
EBOASI 3
 
3.4%
Other values (6) 18
20.7%

Length

2024-05-22T04:30:31.544998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oasetd 8
9.2%
oase 8
9.2%
oasitd 8
9.2%
ocsetd 8
9.2%
ocse 8
9.2%
ocsitd 8
9.2%
ocsi 8
9.2%
oasi 7
 
8.0%
eboasitd 3
 
3.4%
eboasi 3
 
3.4%
Other values (6) 18
20.7%

Most occurring characters

ValueCountFrequency (%)
O 87
18.9%
S 75
16.3%
E 56
12.2%
T 44
9.6%
D 44
9.6%
C 44
9.6%
A 43
9.3%
I 43
9.3%
B 24
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 87
18.9%
S 75
16.3%
E 56
12.2%
T 44
9.6%
D 44
9.6%
C 44
9.6%
A 43
9.3%
I 43
9.3%
B 24
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 87
18.9%
S 75
16.3%
E 56
12.2%
T 44
9.6%
D 44
9.6%
C 44
9.6%
A 43
9.3%
I 43
9.3%
B 24
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 87
18.9%
S 75
16.3%
E 56
12.2%
T 44
9.6%
D 44
9.6%
C 44
9.6%
A 43
9.3%
I 43
9.3%
B 24
 
5.2%

cop_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

cop_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

flag
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
real
87 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters348
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreal
2nd rowreal
3rd rowreal
4th rowreal
5th rowreal

Common Values

ValueCountFrequency (%)
real 87
100.0%

Length

2024-05-22T04:30:31.652832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:30:31.740844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
real 87
100.0%

Most occurring characters

ValueCountFrequency (%)
r 87
25.0%
e 87
25.0%
a 87
25.0%
l 87
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 87
25.0%
e 87
25.0%
a 87
25.0%
l 87
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 87
25.0%
e 87
25.0%
a 87
25.0%
l 87
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 87
25.0%
e 87
25.0%
a 87
25.0%
l 87
25.0%

acc_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

acc_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

rms_acc_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6626801
Minimum0.2348498
Maximum1.9998154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:31.867874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2348498
5-th percentile0.26977363
Q10.39492764
median0.51051802
Q30.81572005
95-th percentile1.6668008
Maximum1.9998154
Range1.7649656
Interquartile range (IQR)0.42079241

Descriptive statistics

Standard deviation0.40905501
Coefficient of variation (CV)0.61727372
Kurtosis1.7802467
Mean0.6626801
Median Absolute Deviation (MAD)0.16223615
Skewness1.5468468
Sum57.653168
Variance0.167326
MonotonicityNot monotonic
2024-05-22T04:30:31.999908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.275943689 1
 
1.1%
0.4868095091 1
 
1.1%
0.6801843989 1
 
1.1%
0.6371557962 1
 
1.1%
0.5586116337 1
 
1.1%
0.2607901215 1
 
1.1%
0.4317389806 1
 
1.1%
0.6877182362 1
 
1.1%
0.483317775 1
 
1.1%
0.4310861274 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.2348497991 1
1.1%
0.2568197723 1
1.1%
0.2589750279 1
1.1%
0.2607901215 1
1.1%
0.2671293192 1
1.1%
0.275943689 1
1.1%
0.2782164761 1
1.1%
0.282134368 1
1.1%
0.2872160436 1
1.1%
0.2995577542 1
1.1%
ValueCountFrequency (%)
1.999815449 1
1.1%
1.742018829 1
1.1%
1.718595743 1
1.1%
1.709604882 1
1.1%
1.668098308 1
1.1%
1.663773298 1
1.1%
1.56766506 1
1.1%
1.486066521 1
1.1%
1.302704945 1
1.1%
1.174964014 1
1.1%

rms_acc_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3795612
Minimum0.49061766
Maximum4.1803657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:32.129943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.49061766
5-th percentile0.6246625
Q10.90537354
median1.2420044
Q31.6225303
95-th percentile2.4193938
Maximum4.1803657
Range3.6897481
Interquartile range (IQR)0.71715672

Descriptive statistics

Standard deviation0.66921563
Coefficient of variation (CV)0.48509311
Kurtosis4.13293
Mean1.3795612
Median Absolute Deviation (MAD)0.37965415
Skewness1.7146446
Sum120.02182
Variance0.44784956
MonotonicityNot monotonic
2024-05-22T04:30:32.259961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.017366181 1
 
1.1%
0.8313235079 1
 
1.1%
1.240036396 1
 
1.1%
0.7996184682 1
 
1.1%
0.9483968684 1
 
1.1%
0.6705876321 1
 
1.1%
0.8608682547 1
 
1.1%
0.5884818412 1
 
1.1%
0.8268646559 1
 
1.1%
0.7687676239 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.4906176645 1
1.1%
0.4998344369 1
1.1%
0.5594979932 1
1.1%
0.5884818412 1
1.1%
0.6129734016 1
1.1%
0.6519370519 1
1.1%
0.6705876321 1
1.1%
0.722486363 1
1.1%
0.7687676239 1
1.1%
0.7688717762 1
1.1%
ValueCountFrequency (%)
4.180365741 1
1.1%
3.455078552 1
1.1%
3.365097485 1
1.1%
3.224441074 1
1.1%
2.440353087 1
1.1%
2.370488746 1
1.1%
2.234887114 1
1.1%
2.151187243 1
1.1%
2.083094536 1
1.1%
2.069838796 1
1.1%

path
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.01317
Minimum90.939643
Maximum684.28383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:32.385998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum90.939643
5-th percentile132.21945
Q1206.3483
median250.77511
Q3359.84887
95-th percentile546.85039
Maximum684.28383
Range593.34418
Interquartile range (IQR)153.50057

Descriptive statistics

Standard deviation130.47478
Coefficient of variation (CV)0.45144925
Kurtosis0.66921452
Mean289.01317
Median Absolute Deviation (MAD)69.144802
Skewness1.051107
Sum25144.146
Variance17023.668
MonotonicityNot monotonic
2024-05-22T04:30:32.513023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212.7012119 1
 
1.1%
168.0441547 1
 
1.1%
377.7912534 1
 
1.1%
238.4148973 1
 
1.1%
225.4866156 1
 
1.1%
146.7052683 1
 
1.1%
280.3302373 1
 
1.1%
355.6419195 1
 
1.1%
226.3615598 1
 
1.1%
165.1903957 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
90.93964261 1
1.1%
103.3439452 1
1.1%
104.1331081 1
1.1%
111.4527056 1
1.1%
130.8285454 1
1.1%
135.4649002 1
1.1%
140.4831835 1
1.1%
146.7052683 1
1.1%
153.8213853 1
1.1%
156.6552388 1
1.1%
ValueCountFrequency (%)
684.2838276 1
1.1%
626.6645057 1
1.1%
592.7504415 1
1.1%
586.808635 1
1.1%
547.3992355 1
1.1%
545.5697489 1
1.1%
539.7168653 1
1.1%
537.6414588 1
1.1%
522.3508133 1
1.1%
483.2764066 1
1.1%

samp_en_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070223623
Minimum0.013173286
Maximum0.30849121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:32.637057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.013173286
5-th percentile0.017567727
Q10.032340663
median0.052230348
Q30.081930674
95-th percentile0.21545012
Maximum0.30849121
Range0.29531793
Interquartile range (IQR)0.049590011

Descriptive statistics

Standard deviation0.058692988
Coefficient of variation (CV)0.83580118
Kurtosis4.3092186
Mean0.070223623
Median Absolute Deviation (MAD)0.020970373
Skewness2.0445364
Sum6.1094552
Variance0.0034448668
MonotonicityNot monotonic
2024-05-22T04:30:32.763085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0226336838 1
 
1.1%
0.0432010777 1
 
1.1%
0.0659691566 1
 
1.1%
0.0657456878 1
 
1.1%
0.0612143515 1
 
1.1%
0.0164089546 1
 
1.1%
0.0335538862 1
 
1.1%
0.0838771387 1
 
1.1%
0.0439503042 1
 
1.1%
0.0377813559 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.0131732859 1
1.1%
0.014981134 1
1.1%
0.0164089546 1
1.1%
0.0166161303 1
1.1%
0.0169495583 1
1.1%
0.0190101207 1
1.1%
0.0190499266 1
1.1%
0.0216658588 1
1.1%
0.0226336838 1
1.1%
0.0233341146 1
1.1%
ValueCountFrequency (%)
0.308491211 1
1.1%
0.2527777899 1
1.1%
0.2385234039 1
1.1%
0.2354579253 1
1.1%
0.2181027066 1
1.1%
0.2092607501 1
1.1%
0.1566097969 1
1.1%
0.1529451179 1
1.1%
0.1517311339 1
1.1%
0.1294196767 1
1.1%

samp_en_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16980792
Minimum0.050859438
Maximum0.40306193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:32.892827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.050859438
5-th percentile0.067874882
Q10.10556943
median0.1501736
Q30.2234956
95-th percentile0.30996795
Maximum0.40306193
Range0.35220249
Interquartile range (IQR)0.11792617

Descriptive statistics

Standard deviation0.082749336
Coefficient of variation (CV)0.48731139
Kurtosis0.093349309
Mean0.16980792
Median Absolute Deviation (MAD)0.058325524
Skewness0.80294853
Sum14.773289
Variance0.0068474526
MonotonicityNot monotonic
2024-05-22T04:30:33.045860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1303536828 1
 
1.1%
0.1066515939 1
 
1.1%
0.174024866 1
 
1.1%
0.0974021021 1
 
1.1%
0.1200353631 1
 
1.1%
0.0769620091 1
 
1.1%
0.108815029 1
 
1.1%
0.0687332368 1
 
1.1%
0.0924488688 1
 
1.1%
0.0749026486 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.0508594385 1
1.1%
0.0536971779 1
1.1%
0.0545026812 1
1.1%
0.0667961011 1
1.1%
0.0675070157 1
1.1%
0.0687332368 1
1.1%
0.0702201767 1
1.1%
0.0749026486 1
1.1%
0.0759153832 1
1.1%
0.0769620091 1
1.1%
ValueCountFrequency (%)
0.4030619274 1
1.1%
0.3994317531 1
1.1%
0.3631013464 1
1.1%
0.3331412923 1
1.1%
0.3114386425 1
1.1%
0.306536349 1
1.1%
0.3034152841 1
1.1%
0.3008548047 1
1.1%
0.2998232036 1
1.1%
0.2927757901 1
1.1%

f80_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.35922
Minimum109.35984
Maximum952.99051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:33.174875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum109.35984
5-th percentile140.34009
Q1190.3754
median288.04583
Q3440.79879
95-th percentile723.9943
Maximum952.99051
Range843.63067
Interquartile range (IQR)250.42339

Descriptive statistics

Standard deviation193.30393
Coefficient of variation (CV)0.56297871
Kurtosis1.150331
Mean343.35922
Median Absolute Deviation (MAD)109.22581
Skewness1.2413235
Sum29872.252
Variance37366.411
MonotonicityNot monotonic
2024-05-22T04:30:33.295389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193.6153459 1
 
1.1%
224.1948061 1
 
1.1%
431.6854217 1
 
1.1%
297.8343051 1
 
1.1%
245.877944 1
 
1.1%
181.0493154 1
 
1.1%
397.2716359 1
 
1.1%
582.3187053 1
 
1.1%
254.7496264 1
 
1.1%
277.6671721 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
109.3598369 1
1.1%
119.8031387 1
1.1%
120.99282 1
1.1%
124.2044299 1
1.1%
139.5812542 1
1.1%
142.1107111 1
1.1%
145.9963266 1
1.1%
154.4660895 1
1.1%
157.4830289 1
1.1%
159.3464627 1
1.1%
ValueCountFrequency (%)
952.9905072 1
1.1%
893.7198401 1
1.1%
853.8753543 1
1.1%
852.2319792 1
1.1%
745.9182398 1
1.1%
672.8384545 1
1.1%
656.6517657 1
1.1%
637.7872226 1
1.1%
618.857502 1
1.1%
608.6868033 1
1.1%

f80_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704.08753
Minimum243.57117
Maximum2036.1352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:33.414415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum243.57117
5-th percentile304.06382
Q1498.69719
median633.48306
Q3827.95178
95-th percentile1278.5317
Maximum2036.1352
Range1792.5641
Interquartile range (IQR)329.2546

Descriptive statistics

Standard deviation322.38951
Coefficient of variation (CV)0.45788272
Kurtosis3.8023026
Mean704.08753
Median Absolute Deviation (MAD)159.14776
Skewness1.6504154
Sum61255.615
Variance103935
MonotonicityNot monotonic
2024-05-22T04:30:33.548447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453.199431 1
 
1.1%
381.1475721 1
 
1.1%
712.2873446 1
 
1.1%
496.7943942 1
 
1.1%
524.3480857 1
 
1.1%
358.2926915 1
 
1.1%
473.3617811 1
 
1.1%
654.2995264 1
 
1.1%
465.9499089 1
 
1.1%
491.5479716 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
243.5711668 1
1.1%
254.2757262 1
1.1%
254.8751147 1
1.1%
279.7814927 1
1.1%
280.8228815 1
1.1%
358.2926915 1
1.1%
381.1475721 1
1.1%
406.0037008 1
1.1%
408.8564825 1
1.1%
416.6465866 1
1.1%
ValueCountFrequency (%)
2036.135246 1
1.1%
1793.99739 1
1.1%
1555.35536 1
1.1%
1319.09604 1
1.1%
1306.061002 1
1.1%
1214.296527 1
1.1%
1206.962861 1
1.1%
1195.636925 1
1.1%
1183.459761 1
1.1%
1150.034404 1
1.1%

mf_lf_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.073086
Minimum6.1998743
Maximum72.231048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:33.674514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.1998743
5-th percentile8.1190863
Q112.061963
median19.273246
Q331.511451
95-th percentile55.197552
Maximum72.231048
Range66.031174
Interquartile range (IQR)19.449488

Descriptive statistics

Standard deviation15.205845
Coefficient of variation (CV)0.63165332
Kurtosis1.6884676
Mean24.073086
Median Absolute Deviation (MAD)8.0185129
Skewness1.375697
Sum2094.3585
Variance231.21771
MonotonicityNot monotonic
2024-05-22T04:30:33.799549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.40274828 1
 
1.1%
15.02434952 1
 
1.1%
37.86566733 1
 
1.1%
20.77875317 1
 
1.1%
16.39436635 1
 
1.1%
11.67018271 1
 
1.1%
32.5979287 1
 
1.1%
42.51393238 1
 
1.1%
20.64492769 1
 
1.1%
16.7950673 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
6.199874265 1
1.1%
7.54502215 1
1.1%
7.696101742 1
1.1%
7.713435581 1
1.1%
8.016617071 1
1.1%
8.358181211 1
1.1%
9.130397208 1
1.1%
9.34876324 1
1.1%
9.563270019 1
1.1%
10.24903526 1
1.1%
ValueCountFrequency (%)
72.23104844 1
1.1%
70.30614441 1
1.1%
66.2889485 1
1.1%
66.12173891 1
1.1%
56.12278633 1
1.1%
53.03867081 1
1.1%
50.25890232 1
1.1%
48.73172089 1
1.1%
45.210066 1
1.1%
43.12764474 1
1.1%

mf_lf_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.554693
Minimum14.204995
Maximum156.37712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:33.919576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14.204995
5-th percentile19.767
Q132.963818
median42.931373
Q354.363708
95-th percentile97.798809
Maximum156.37712
Range142.17212
Interquartile range (IQR)21.39989

Descriptive statistics

Standard deviation25.367821
Coefficient of variation (CV)0.52245868
Kurtosis4.3106499
Mean48.554693
Median Absolute Deviation (MAD)11.077239
Skewness1.8173477
Sum4224.2583
Variance643.52634
MonotonicityNot monotonic
2024-05-22T04:30:34.041597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.14621442 1
 
1.1%
28.51365039 1
 
1.1%
56.6013396 1
 
1.1%
44.67334341 1
 
1.1%
37.77920236 1
 
1.1%
28.67614893 1
 
1.1%
39.1060416 1
 
1.1%
52.85507875 1
 
1.1%
39.99370095 1
 
1.1%
27.54502545 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
14.20499533 1
1.1%
15.02871622 1
1.1%
18.61363971 1
1.1%
18.77083625 1
1.1%
19.49706046 1
1.1%
20.39685915 1
1.1%
22.24208746 1
1.1%
24.10058634 1
1.1%
24.32723073 1
1.1%
26.14621442 1
1.1%
ValueCountFrequency (%)
156.377117 1
1.1%
133.0278051 1
1.1%
110.5797639 1
1.1%
109.5014892 1
1.1%
98.05065924 1
1.1%
97.21115864 1
1.1%
94.98480661 1
1.1%
91.18909344 1
1.1%
78.11965931 1
1.1%
76.23360739 1
1.1%

mf_mf_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2856032
Minimum1.4994864
Maximum18.091356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:34.164623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.4994864
5-th percentile2.2815475
Q12.9823843
median4.2074454
Q36.15176
95-th percentile13.042828
Maximum18.091356
Range16.591869
Interquartile range (IQR)3.1693757

Descriptive statistics

Standard deviation3.4423061
Coefficient of variation (CV)0.6512608
Kurtosis2.9518532
Mean5.2856032
Median Absolute Deviation (MAD)1.4003694
Skewness1.7853101
Sum459.84748
Variance11.849471
MonotonicityNot monotonic
2024-05-22T04:30:34.510688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.098147208 1
 
1.1%
3.732991706 1
 
1.1%
4.483395298 1
 
1.1%
4.703804904 1
 
1.1%
4.451534621 1
 
1.1%
2.6310682 1
 
1.1%
4.079539347 1
 
1.1%
7.540311866 1
 
1.1%
3.423226154 1
 
1.1%
4.462736106 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
1.499486395 1
1.1%
1.681162834 1
1.1%
2.052502599 1
1.1%
2.193603761 1
1.1%
2.20059764 1
1.1%
2.470430612 1
1.1%
2.494176288 1
1.1%
2.498316531 1
1.1%
2.620792459 1
1.1%
2.6310682 1
1.1%
ValueCountFrequency (%)
18.09135558 1
1.1%
15.72930541 1
1.1%
15.32460701 1
1.1%
14.06394794 1
1.1%
13.47799088 1
1.1%
12.02744694 1
1.1%
11.30212634 1
1.1%
11.15516752 1
1.1%
11.11444099 1
1.1%
10.54254282 1
1.1%

mf_mf_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.064051
Minimum3.794569
Maximum28.882994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:34.653986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.794569
5-th percentile4.6783538
Q16.9575898
median10.045229
Q313.750101
95-th percentile20.319414
Maximum28.882994
Range25.088425
Interquartile range (IQR)6.7925108

Descriptive statistics

Standard deviation5.3002771
Coefficient of variation (CV)0.47905392
Kurtosis2.043877
Mean11.064051
Median Absolute Deviation (MAD)3.3797735
Skewness1.2763904
Sum962.57246
Variance28.092938
MonotonicityNot monotonic
2024-05-22T04:30:34.792021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.07130873 1
 
1.1%
5.530378246 1
 
1.1%
9.41873267 1
 
1.1%
4.641499959 1
 
1.1%
7.478680975 1
 
1.1%
4.636110027 1
 
1.1%
5.703299721 1
 
1.1%
6.594936305 1
 
1.1%
5.300682579 1
 
1.1%
8.145808819 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
3.794568979 1
1.1%
4.492384453 1
1.1%
4.562861375 1
1.1%
4.636110027 1
1.1%
4.641499959 1
1.1%
4.764346036 1
1.1%
4.785226477 1
1.1%
4.813527276 1
1.1%
5.282464969 1
1.1%
5.300682579 1
1.1%
ValueCountFrequency (%)
28.88299409 1
1.1%
27.76282559 1
1.1%
26.70088134 1
1.1%
25.14700788 1
1.1%
21.10346054 1
1.1%
18.48997246 1
1.1%
18.29106285 1
1.1%
17.37930429 1
1.1%
17.17780601 1
1.1%
16.85579918 1
1.1%

mf_hf_x
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74750185
Minimum0.141514
Maximum2.4124335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:34.927190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.141514
5-th percentile0.23070435
Q10.32932742
median0.60854919
Q30.91076251
95-th percentile1.8973426
Maximum2.4124335
Range2.2709195
Interquartile range (IQR)0.58143508

Descriptive statistics

Standard deviation0.53098282
Coefficient of variation (CV)0.71034315
Kurtosis1.2414025
Mean0.74750185
Median Absolute Deviation (MAD)0.27951494
Skewness1.3397388
Sum65.032661
Variance0.28194276
MonotonicityNot monotonic
2024-05-22T04:30:35.058219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8761820839 1
 
1.1%
0.4502565335 1
 
1.1%
0.6612061234 1
 
1.1%
0.5847800633 1
 
1.1%
0.2464431967 1
 
1.1%
0.7669442785 1
 
1.1%
1.205601935 1
 
1.1%
1.913742547 1
 
1.1%
0.2322742865 1
 
1.1%
1.131280226 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.1415139987 1
1.1%
0.1524685093 1
1.1%
0.171580051 1
1.1%
0.2219386052 1
1.1%
0.2300315138 1
1.1%
0.2322742865 1
1.1%
0.2403947454 1
1.1%
0.2443218567 1
1.1%
0.2464431967 1
1.1%
0.2524661825 1
1.1%
ValueCountFrequency (%)
2.412433465 1
1.1%
2.30595679 1
1.1%
2.070021301 1
1.1%
2.002375198 1
1.1%
1.913742547 1
1.1%
1.859076089 1
1.1%
1.765983878 1
1.1%
1.681488653 1
1.1%
1.634772945 1
1.1%
1.614698079 1
1.1%

mf_hf_y
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5660235
Minimum0.24465549
Maximum6.2201651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-22T04:30:35.185249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.24465549
5-th percentile0.51101835
Q10.87395975
median1.4268678
Q31.9963154
95-th percentile2.9068627
Maximum6.2201651
Range5.9755096
Interquartile range (IQR)1.1223557

Descriptive statistics

Standard deviation0.90996499
Coefficient of variation (CV)0.58106725
Kurtosis6.5115165
Mean1.5660235
Median Absolute Deviation (MAD)0.56729744
Skewness1.6954377
Sum136.24405
Variance0.82803627
MonotonicityNot monotonic
2024-05-22T04:30:35.319279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.822447065 1
 
1.1%
0.634665344 1
 
1.1%
1.044595115 1
 
1.1%
0.8753639964 1
 
1.1%
1.332066357 1
 
1.1%
0.5505268573 1
 
1.1%
0.7385704701 1
 
1.1%
2.286888283 1
 
1.1%
0.5869974823 1
 
1.1%
2.365261136 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
0.2446554853 1
1.1%
0.3548823751 1
1.1%
0.4597078011 1
1.1%
0.4796162638 1
1.1%
0.4940861261 1
1.1%
0.5505268573 1
1.1%
0.5509470818 1
1.1%
0.5668611519 1
1.1%
0.5811734056 1
1.1%
0.5869974823 1
1.1%
ValueCountFrequency (%)
6.220165073 1
1.1%
3.238041458 1
1.1%
3.072222485 1
1.1%
3.034896106 1
1.1%
2.921503183 1
1.1%
2.872701601 1
1.1%
2.865799427 1
1.1%
2.775847646 1
1.1%
2.732364565 1
1.1%
2.678517428 1
1.1%

Interactions

2024-05-22T04:30:28.952709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:15.909699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:17.077232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:18.368555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:19.397804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:20.440972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:21.544724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:22.554547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:23.849316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:24.869628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:25.942096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T04:30:28.878707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

classfile_pathexamcop_xcop_yflagacc_xacc_yrms_acc_xrms_acc_ypathsamp_en_xsamp_en_yf80_xf80_ymf_lf_xmf_lf_ymf_mf_xmf_mf_ymf_hf_xmf_hf_y
0Healthy../data/raw/COP_Tests/HG\participant_21\OASETD_SWARII.txtOASETD[-0.5907142773, -0.5830138725, -0.5683751717000001, -0.5426955712, -0.5182121344, -0.4940375735, -0.46458661360000003, -0.42651236880000004, -0.3905964327, -0.3586407172, -0.3260136031, -0.3028408629, -0.2902497894, -0.2912674777, -0.29789369590000003, -0.30801125700000004, -0.32324050200000004, -0.3363493119, -0.34534962690000004, -0.3519177784, -0.3570344795, -0.35963175480000004, -0.35966674400000004, -0.3592809462, -0.35811229650000004, -0.3551937436, -0.3525539796, -0.3536233023, -0.3540039204, -0.3579956782, -0.3616993676, -0.3682859349, -0.37443396840000004, -0.37823047590000003, -0.37475613690000004, -0.3662966239, -0.35124286460000004, -0.33677071070000003, -0.3268493514, -0.3152525526, -0.3095158852, -0.30699743220000003, -0.3108064674, -0.3152310443, -0.3152356423, -0.31032060570000003, -0.3074028023, -0.3056823442, -0.30412150460000004, -0.30286139700000003, -0.3071461189, -0.3150058201, -0.3205837829, 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